close-trade

Close Trade & Post-Mortem

Close trade $ARGUMENTS and run a post-mortem analysis.

Workflow

Step 1: Close the Trade

Delegate using the Task tool with subagent_type: general-purpose and model: opus:

"You are the portfolio-manager agent. Read agents/portfolio-manager.md for your decision framework. Close trade $ARGUMENTS. If a price is specified after 'at', use that as exit price. Otherwise, get the current market price using get_exchange_prices() from crypto-exchange MCP. Call close_trade(trade_id='...', exit_price=..., close_reason='...') from crypto-learning-db MCP. PnL, portfolio balance, and stats are updated automatically. Do NOT use the Edit tool."

Step 2: Post-Mortem Analysis

After the trade is closed, delegate using the Task tool with subagent_type: general-purpose and model: opus:

"You are the learning-agent. Read agents/learning-agent.md for your analysis framework. Run a post-mortem analysis on the recently closed trade $ARGUMENTS. Call query_trades(status='closed', limit=1) from crypto-learning-db to get the trade data. Read any related reports from data/reports/. Analyze what worked, what didn't, and provide specific recommendations for improvement. Do NOT use the Edit tool."

Step 3: Validate Predictions & Update Patterns

After the post-mortem, delegate using the Task tool with subagent_type: general-purpose and model: opus:

"You are the learning-agent. Validate all predictions for trade $ARGUMENTS. Call query_predictions(trade_id='...') from crypto-learning-db to find all predictions tied to this trade. Compare each prediction against the actual outcome. Call validate_prediction() for each one with a detailed NL evaluation of how close the prediction was and what we can learn. Then call upsert_pattern() to update the pattern library with the setup from this trade. Do NOT use the Edit tool."

Step 4: Present Results

Show:

1. Trade closure summary (entry, exit, PnL)
2. Post-mortem analysis
3. Prediction accuracy (how many correct vs incorrect, with evaluations)
4. Pattern identified (win rate, recommendation)
5. Lessons learned